Multisensor data fusion: concepts and principles

Multisensor data fusion is concerned with the integration and extraction of information from data obtained by two or more sensors. Assuming the data contaminated with noise, the authors present the necessary definitions and concepts to formulate multisensor data fusion as a problem of inference. The types of problems addressed include detection, resolution, discrimination, and parameter estimation. Specifically, the authors show how to assign probabilities to hypotheses (propositions) when data from different sensors supply information relevant to the hypotheses. Several examples involving two-sensor data fusion are discussed.<>